{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 准备阶段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests,json\n",
    "import pandas as pd\n",
    "import cufflinks as cf\n",
    "import plotly as py\n",
    "import plotly.graph_objs as go\n",
    "import io\n",
    "import base64\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import Bar"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入选择的Excel文档"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 241 entries, 0 to 240\n",
      "Data columns (total 42 columns):\n",
      " #   Column                                  Non-Null Count  Dtype \n",
      "---  ------                                  --------------  ----- \n",
      " 0   1、您的性别：                                 241 non-null    object\n",
      " 1   2、您的年龄段：                                241 non-null    object\n",
      " 2   3、您现在的居住地是：                             241 non-null    object\n",
      " 3   4、您的婚姻状况是：                              241 non-null    object\n",
      " 4   5、您的居住方式是：                              241 non-null    object\n",
      " 5   6、您的文化程度是：                              241 non-null    object\n",
      " 6   7、您现在的每个月收入（不包括子女给的）：                   241 non-null    object\n",
      " 7   8、您的子女个数：                               241 non-null    object\n",
      " 8   9、您和孩子之间的关系：                            241 non-null    object\n",
      " 9   10、您目前的身体状况：                            241 non-null    object\n",
      " 10  11、您是否经常和朋友一起进行娱乐活动                     241 non-null    object\n",
      " 11  12、您对居住地现有提供的养老服务有什么看法                  241 non-null    object\n",
      " 12  13、您是否有自己的兴趣爱好                          241 non-null    object\n",
      " 13  14、(PA您最近对生活特别满意)                       241 non-null    int64 \n",
      " 14  14、(PA您最近的情绪很好)                         241 non-null    int64 \n",
      " 15  14、(PA您最近对生活非常满意)                       241 non-null    int64 \n",
      " 16  14、(PA您最近很幸运)                           241 non-null    int64 \n",
      " 17  14、(NA您最近感到很烦恼)                         241 non-null    int64 \n",
      " 18  14、(NA您最近感觉很孤独或与人疏远)                    241 non-null    int64 \n",
      " 19  14、(NA您最近非常的忧虑或不愉快)                     241 non-null    int64 \n",
      " 20  14、(NA您最近总是很担心，因为不知道将会发生什么情况)           241 non-null    int64 \n",
      " 21  14、(NA您最近感到您的生活处境变得艰苦)                  241 non-null    int64 \n",
      " 22  14、(PA您最近的生活处境变化使你感到满意)                 241 non-null    int64 \n",
      " 23  14、(NE您感觉最近是一生中最困难的时期)                  241 non-null    int64 \n",
      " 24  14、(PE您像年轻时一样开心)                        241 non-null    int64 \n",
      " 25  14、(NE您最近所做的大部分事情都令人厌烦或单调)              241 non-null    int64 \n",
      " 26  14、(PE您最近做的事情像以前一样是您感兴趣)                241 non-null    int64 \n",
      " 27  14、(PE您回顾您的一生时，感到非常的满意)                 241 non-null    int64 \n",
      " 28  14、(NE随着年龄的增加，您感觉一切事情变得更加糟糕)            241 non-null    int64 \n",
      " 29  14、(NE您时常感到很低落)                         241 non-null    int64 \n",
      " 30  14、(NE最近您对诸事比较烦恼)                       241 non-null    int64 \n",
      " 31  14、(PE如果您有机会到想住的地方居住，您愿意离开现在的地方到那儿去居住)  241 non-null    int64 \n",
      " 32  14、(NE您有时候感觉对生活没有了希望和期待)                241 non-null    int64 \n",
      " 33  14、(PE您最近像年轻时一样高兴)                      241 non-null    int64 \n",
      " 34  14、(NE大多时候您感觉生活是艰苦的)                    241 non-null    int64 \n",
      " 35  14、(PE您对目前的生活比较满意)                      241 non-null    int64 \n",
      " 36  14、(PE您的身体状况比同龄人差不多甚至好于他人)              241 non-null    int64 \n",
      " 37  总分                                      241 non-null    int64 \n",
      " 38  PA总分                                    241 non-null    int64 \n",
      " 39  NA总分                                    241 non-null    int64 \n",
      " 40  PE总分                                    241 non-null    int64 \n",
      " 41  NE总分                                    241 non-null    int64 \n",
      "dtypes: int64(29), object(13)\n",
      "memory usage: 79.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_excel(\"幸福感分析.xlsx\", encoding = \"utf8\", sep=\"\\t\")\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1、您的性别：</th>\n",
       "      <th>2、您的年龄段：</th>\n",
       "      <th>3、您现在的居住地是：</th>\n",
       "      <th>4、您的婚姻状况是：</th>\n",
       "      <th>5、您的居住方式是：</th>\n",
       "      <th>6、您的文化程度是：</th>\n",
       "      <th>7、您现在的每个月收入（不包括子女给的）：</th>\n",
       "      <th>8、您的子女个数：</th>\n",
       "      <th>9、您和孩子之间的关系：</th>\n",
       "      <th>10、您目前的身体状况：</th>\n",
       "      <th>...</th>\n",
       "      <th>14、(NE您有时候感觉对生活没有了希望和期待)</th>\n",
       "      <th>14、(PE您最近像年轻时一样高兴)</th>\n",
       "      <th>14、(NE大多时候您感觉生活是艰苦的)</th>\n",
       "      <th>14、(PE您对目前的生活比较满意)</th>\n",
       "      <th>14、(PE您的身体状况比同龄人差不多甚至好于他人)</th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>男</td>\n",
       "      <td>80岁以上</td>\n",
       "      <td>西北地区（陕西省、甘肃省、青海省、宁夏回族自治区、新疆维吾尔自治区）</td>\n",
       "      <td>未结过婚</td>\n",
       "      <td>独自一个人居住</td>\n",
       "      <td>文盲</td>\n",
       "      <td>1000元以下</td>\n",
       "      <td>没有孩子</td>\n",
       "      <td>关系较差</td>\n",
       "      <td>身体不好</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>女</td>\n",
       "      <td>60-69岁</td>\n",
       "      <td>华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）</td>\n",
       "      <td>离婚</td>\n",
       "      <td>独自一个人居住</td>\n",
       "      <td>大专或者本科</td>\n",
       "      <td>5500-7000元</td>\n",
       "      <td>1个</td>\n",
       "      <td>很亲近</td>\n",
       "      <td>非常健康</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>女</td>\n",
       "      <td>60-69岁</td>\n",
       "      <td>西南地区（四川省、贵州省、云南省、重庆市、西藏自治区）</td>\n",
       "      <td>丧偶</td>\n",
       "      <td>独自一个人居住</td>\n",
       "      <td>文盲</td>\n",
       "      <td>1000元以下</td>\n",
       "      <td>2个</td>\n",
       "      <td>关系一般</td>\n",
       "      <td>身体不好</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>男</td>\n",
       "      <td>60-69岁</td>\n",
       "      <td>东北地区（黑龙江省、吉林省、辽宁省）</td>\n",
       "      <td>丧偶</td>\n",
       "      <td>独自一个人居住</td>\n",
       "      <td>文盲</td>\n",
       "      <td>1000-2500元</td>\n",
       "      <td>2个</td>\n",
       "      <td>关系较差</td>\n",
       "      <td>身体不好</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>女</td>\n",
       "      <td>70-79岁</td>\n",
       "      <td>华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）</td>\n",
       "      <td>已婚</td>\n",
       "      <td>和配偶居住在一起</td>\n",
       "      <td>大专或者本科</td>\n",
       "      <td>1000-2500元</td>\n",
       "      <td>1个</td>\n",
       "      <td>比较亲近</td>\n",
       "      <td>非常健康</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>女</td>\n",
       "      <td>80岁以上</td>\n",
       "      <td>华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）</td>\n",
       "      <td>丧偶</td>\n",
       "      <td>独自一个人居住</td>\n",
       "      <td>初中</td>\n",
       "      <td>1000-2500元</td>\n",
       "      <td>2个</td>\n",
       "      <td>很亲近</td>\n",
       "      <td>一般</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>女</td>\n",
       "      <td>60-69岁</td>\n",
       "      <td>华中地区（河南省、湖北省、湖南省）</td>\n",
       "      <td>已婚</td>\n",
       "      <td>和子女居住在一起</td>\n",
       "      <td>小学</td>\n",
       "      <td>1000-2500元</td>\n",
       "      <td>3个及以上</td>\n",
       "      <td>比较亲近</td>\n",
       "      <td>比较健康</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>44</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>男</td>\n",
       "      <td>60-69岁</td>\n",
       "      <td>华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）</td>\n",
       "      <td>已婚</td>\n",
       "      <td>和配偶居住在一起</td>\n",
       "      <td>小学</td>\n",
       "      <td>2500-4000元</td>\n",
       "      <td>2个</td>\n",
       "      <td>比较亲近</td>\n",
       "      <td>比较健康</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>男</td>\n",
       "      <td>80岁以上</td>\n",
       "      <td>华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）</td>\n",
       "      <td>未结过婚</td>\n",
       "      <td>独自一个人居住</td>\n",
       "      <td>大专或者本科</td>\n",
       "      <td>1000元以下</td>\n",
       "      <td>没有孩子</td>\n",
       "      <td>关系一般</td>\n",
       "      <td>比较健康</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>37</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>女</td>\n",
       "      <td>60-69岁</td>\n",
       "      <td>华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）</td>\n",
       "      <td>已婚</td>\n",
       "      <td>和配偶居住在一起</td>\n",
       "      <td>初中</td>\n",
       "      <td>5500-7000元</td>\n",
       "      <td>1个</td>\n",
       "      <td>比较亲近</td>\n",
       "      <td>比较健康</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>241 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    1、您的性别： 2、您的年龄段：                            3、您现在的居住地是： 4、您的婚姻状况是：  \\\n",
       "0         男    80岁以上     西北地区（陕西省、甘肃省、青海省、宁夏回族自治区、新疆维吾尔自治区）       未结过婚   \n",
       "1         女   60-69岁  华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）         离婚   \n",
       "2         女   60-69岁            西南地区（四川省、贵州省、云南省、重庆市、西藏自治区）         丧偶   \n",
       "3         男   60-69岁                     东北地区（黑龙江省、吉林省、辽宁省）         丧偶   \n",
       "4         女   70-79岁  华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）         已婚   \n",
       "..      ...      ...                                    ...        ...   \n",
       "236       女    80岁以上  华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）         丧偶   \n",
       "237       女   60-69岁                      华中地区（河南省、湖北省、湖南省）         已婚   \n",
       "238       男   60-69岁  华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）         已婚   \n",
       "239       男    80岁以上  华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）       未结过婚   \n",
       "240       女   60-69岁  华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）         已婚   \n",
       "\n",
       "    5、您的居住方式是： 6、您的文化程度是： 7、您现在的每个月收入（不包括子女给的）： 8、您的子女个数： 9、您和孩子之间的关系：  \\\n",
       "0      独自一个人居住         文盲               1000元以下      没有孩子         关系较差   \n",
       "1      独自一个人居住     大专或者本科            5500-7000元        1个          很亲近   \n",
       "2      独自一个人居住         文盲               1000元以下        2个         关系一般   \n",
       "3      独自一个人居住         文盲            1000-2500元        2个         关系较差   \n",
       "4     和配偶居住在一起     大专或者本科            1000-2500元        1个         比较亲近   \n",
       "..         ...        ...                   ...       ...          ...   \n",
       "236    独自一个人居住         初中            1000-2500元        2个          很亲近   \n",
       "237   和子女居住在一起         小学            1000-2500元     3个及以上         比较亲近   \n",
       "238   和配偶居住在一起         小学            2500-4000元        2个         比较亲近   \n",
       "239    独自一个人居住     大专或者本科               1000元以下      没有孩子         关系一般   \n",
       "240   和配偶居住在一起         初中            5500-7000元        1个         比较亲近   \n",
       "\n",
       "    10、您目前的身体状况：  ... 14、(NE您有时候感觉对生活没有了希望和期待) 14、(PE您最近像年轻时一样高兴)  \\\n",
       "0           身体不好  ...                        2                  2   \n",
       "1           非常健康  ...                        0                  2   \n",
       "2           身体不好  ...                        0                  0   \n",
       "3           身体不好  ...                        1                  2   \n",
       "4           非常健康  ...                        1                  1   \n",
       "..           ...  ...                      ...                ...   \n",
       "236           一般  ...                        1                  2   \n",
       "237         比较健康  ...                        0                  1   \n",
       "238         比较健康  ...                        0                  1   \n",
       "239         比较健康  ...                        0                  2   \n",
       "240         比较健康  ...                        1                  1   \n",
       "\n",
       "    14、(NE大多时候您感觉生活是艰苦的)  14、(PE您对目前的生活比较满意)  14、(PE您的身体状况比同龄人差不多甚至好于他人)  总分  \\\n",
       "0                      2                   2                           2  24   \n",
       "1                      0                   2                           2  42   \n",
       "2                      2                   2                           0  19   \n",
       "3                      2                   1                           1  23   \n",
       "4                      2                   1                           2  28   \n",
       "..                   ...                 ...                         ...  ..   \n",
       "236                    2                   2                           0  37   \n",
       "237                    0                   2                           2  44   \n",
       "238                    2                   1                           1  27   \n",
       "239                    1                   2                           2  37   \n",
       "240                    1                   1                           1  27   \n",
       "\n",
       "     PA总分  NA总分  PE总分  NE总分  \n",
       "0      10    10    14    14  \n",
       "1      10     0    12     4  \n",
       "2       1     6     5     5  \n",
       "3       5     5     8     9  \n",
       "4       7     2    10    11  \n",
       "..    ...   ...   ...   ...  \n",
       "236    10     0    10     7  \n",
       "237     9     0    12     1  \n",
       "238     6     5     8     6  \n",
       "239     9     4    14     6  \n",
       "240     8     4     7     8  \n",
       "\n",
       "[241 rows x 42 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(241)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 选择部分关键词进行分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "性别 = df.groupby ( by = ['1、您的性别：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "年龄 = df.groupby ( by = ['2、您的年龄段：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "居住地 = df.groupby ( by = ['3、您现在的居住地是：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "婚姻 = df.groupby ( by = ['4、您的婚姻状况是：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "居住方式 = df.groupby ( by = ['5、您的居住方式是：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "文化 = df.groupby ( by = ['6、您的文化程度是：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "收入 = df.groupby ( by = ['7、您现在的每个月收入（不包括子女给的）：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "子女关系 = df.groupby ( by = ['9、您和孩子之间的关系：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "身体 = df.groupby ( by = ['10、您目前的身体状况：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "娱乐 = df.groupby ( by = ['11、您是否经常和朋友一起进行娱乐活动'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )\n",
    "兴趣 = df.groupby ( by = ['13、您是否有自己的兴趣爱好'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\"], \\\n",
    "                     \"PA总分\":[\"mean\"], \\\n",
    "                     \"NA总分\":[\"mean\"], \\\n",
    "                     \"PE总分\":[\"mean\"], \\\n",
    "                     \"NE总分\":[\"mean\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\"} )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1、您的性别：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>女</th>\n",
       "      <td>25.352</td>\n",
       "      <td>5.424000</td>\n",
       "      <td>4.616000</td>\n",
       "      <td>7.12800</td>\n",
       "      <td>6.584000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>男</th>\n",
       "      <td>24.500</td>\n",
       "      <td>5.034483</td>\n",
       "      <td>4.732759</td>\n",
       "      <td>7.12069</td>\n",
       "      <td>6.922414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             总分      PA总分      NA总分     PE总分      NE总分\n",
       "             均值        均值        均值       均值        均值\n",
       "1、您的性别：                                               \n",
       "女        25.352  5.424000  4.616000  7.12800  6.584000\n",
       "男        24.500  5.034483  4.732759  7.12069  6.922414"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2、您的年龄段：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60-69岁</th>\n",
       "      <td>24.280000</td>\n",
       "      <td>5.093333</td>\n",
       "      <td>4.986667</td>\n",
       "      <td>6.893333</td>\n",
       "      <td>6.720000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79岁</th>\n",
       "      <td>25.328947</td>\n",
       "      <td>5.039474</td>\n",
       "      <td>4.342105</td>\n",
       "      <td>7.276316</td>\n",
       "      <td>6.644737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80岁以上</th>\n",
       "      <td>25.166667</td>\n",
       "      <td>5.522222</td>\n",
       "      <td>4.688889</td>\n",
       "      <td>7.188889</td>\n",
       "      <td>6.855556</td>\n",
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       "                 总分      PA总分      NA总分      PE总分      NE总分\n",
       "                 均值        均值        均值        均值        均值\n",
       "2、您的年龄段：                                                   \n",
       "60-69岁    24.280000  5.093333  4.986667  6.893333  6.720000\n",
       "70-79岁    25.328947  5.039474  4.342105  7.276316  6.644737\n",
       "80岁以上     25.166667  5.522222  4.688889  7.188889  6.855556"
      ]
     },
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     "output_type": "display_data"
    },
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3、您现在的居住地是：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>东北地区（黑龙江省、吉林省、辽宁省）</th>\n",
       "      <td>23.595238</td>\n",
       "      <td>4.785714</td>\n",
       "      <td>4.952381</td>\n",
       "      <td>7.285714</td>\n",
       "      <td>7.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）</th>\n",
       "      <td>26.030303</td>\n",
       "      <td>5.530303</td>\n",
       "      <td>3.969697</td>\n",
       "      <td>6.893939</td>\n",
       "      <td>6.424242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>华中地区（河南省、湖北省、湖南省）</th>\n",
       "      <td>26.588235</td>\n",
       "      <td>5.235294</td>\n",
       "      <td>4.352941</td>\n",
       "      <td>6.941176</td>\n",
       "      <td>5.235294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>华北地区（北京市、天津市、山西省、河北省、内蒙古自治区）</th>\n",
       "      <td>24.527273</td>\n",
       "      <td>4.927273</td>\n",
       "      <td>5.018182</td>\n",
       "      <td>7.327273</td>\n",
       "      <td>6.709091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>华南地区（广东省、广西壮族自治区、海南省、香港特别行政区、澳门特别行政区）</th>\n",
       "      <td>25.434783</td>\n",
       "      <td>5.478261</td>\n",
       "      <td>4.956522</td>\n",
       "      <td>7.565217</td>\n",
       "      <td>6.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西北地区（陕西省、甘肃省、青海省、宁夏回族自治区、新疆维吾尔自治区）</th>\n",
       "      <td>24.238095</td>\n",
       "      <td>5.857143</td>\n",
       "      <td>5.047619</td>\n",
       "      <td>6.809524</td>\n",
       "      <td>7.380952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>西南地区（四川省、贵州省、云南省、重庆市、西藏自治区）</th>\n",
       "      <td>23.941176</td>\n",
       "      <td>5.117647</td>\n",
       "      <td>5.058824</td>\n",
       "      <td>6.941176</td>\n",
       "      <td>7.058824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              总分      PA总分      NA总分  \\\n",
       "                                              均值        均值        均值   \n",
       "3、您现在的居住地是：                                                            \n",
       "东北地区（黑龙江省、吉林省、辽宁省）                     23.595238  4.785714  4.952381   \n",
       "华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）  26.030303  5.530303  3.969697   \n",
       "华中地区（河南省、湖北省、湖南省）                      26.588235  5.235294  4.352941   \n",
       "华北地区（北京市、天津市、山西省、河北省、内蒙古自治区）           24.527273  4.927273  5.018182   \n",
       "华南地区（广东省、广西壮族自治区、海南省、香港特别行政区、澳门特别行政区）  25.434783  5.478261  4.956522   \n",
       "西北地区（陕西省、甘肃省、青海省、宁夏回族自治区、新疆维吾尔自治区）     24.238095  5.857143  5.047619   \n",
       "西南地区（四川省、贵州省、云南省、重庆市、西藏自治区）            23.941176  5.117647  5.058824   \n",
       "\n",
       "                                           PE总分      NE总分  \n",
       "                                             均值        均值  \n",
       "3、您现在的居住地是：                                                \n",
       "东北地区（黑龙江省、吉林省、辽宁省）                     7.285714  7.523810  \n",
       "华东地区（上海市、江苏省、浙江省、安徽省、福建省、江西省、山东省、台湾省）  6.893939  6.424242  \n",
       "华中地区（河南省、湖北省、湖南省）                      6.941176  5.235294  \n",
       "华北地区（北京市、天津市、山西省、河北省、内蒙古自治区）           7.327273  6.709091  \n",
       "华南地区（广东省、广西壮族自治区、海南省、香港特别行政区、澳门特别行政区）  7.565217  6.652174  \n",
       "西北地区（陕西省、甘肃省、青海省、宁夏回族自治区、新疆维吾尔自治区）     6.809524  7.380952  \n",
       "西南地区（四川省、贵州省、云南省、重庆市、西藏自治区）            6.941176  7.058824  "
      ]
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4、您的婚姻状况是：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>丧偶</th>\n",
       "      <td>28.040000</td>\n",
       "      <td>6.080000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>8.160000</td>\n",
       "      <td>5.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>已婚</th>\n",
       "      <td>24.617347</td>\n",
       "      <td>5.158163</td>\n",
       "      <td>4.678571</td>\n",
       "      <td>6.943878</td>\n",
       "      <td>6.806122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>未结过婚</th>\n",
       "      <td>26.600000</td>\n",
       "      <td>6.800000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>8.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>离婚</th>\n",
       "      <td>23.466667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>6.866667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "                   总分      PA总分      NA总分       PE总分      NE总分\n",
       "                   均值        均值        均值         均值        均值\n",
       "4、您的婚姻状况是：                                                    \n",
       "丧偶          28.040000  6.080000  4.400000   8.160000  5.800000\n",
       "已婚          24.617347  5.158163  4.678571   6.943878  6.806122\n",
       "未结过婚        26.600000  6.800000  6.000000  10.600000  8.800000\n",
       "离婚          23.466667  4.333333  4.600000   6.600000  6.866667"
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5、您的居住方式是：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>住在养老院</th>\n",
       "      <td>24.189189</td>\n",
       "      <td>4.972973</td>\n",
       "      <td>4.972973</td>\n",
       "      <td>6.540541</td>\n",
       "      <td>6.351351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>和子女居住在一起</th>\n",
       "      <td>24.903846</td>\n",
       "      <td>5.192308</td>\n",
       "      <td>4.519231</td>\n",
       "      <td>7.365385</td>\n",
       "      <td>7.134615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>和配偶居住在一起</th>\n",
       "      <td>24.715789</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>4.642105</td>\n",
       "      <td>7.010526</td>\n",
       "      <td>6.852632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>独自一个人居住</th>\n",
       "      <td>25.842105</td>\n",
       "      <td>5.508772</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>7.473684</td>\n",
       "      <td>6.473684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "                   总分      PA总分      NA总分      PE总分      NE总分\n",
       "                   均值        均值        均值        均值        均值\n",
       "5、您的居住方式是：                                                   \n",
       "住在养老院       24.189189  4.972973  4.972973  6.540541  6.351351\n",
       "和子女居住在一起    24.903846  5.192308  4.519231  7.365385  7.134615\n",
       "和配偶居住在一起    24.715789  5.200000  4.642105  7.010526  6.852632\n",
       "独自一个人居住     25.842105  5.508772  4.666667  7.473684  6.473684"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6、您的文化程度是：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>中专或技校</th>\n",
       "      <td>24.032258</td>\n",
       "      <td>5.096774</td>\n",
       "      <td>5.161290</td>\n",
       "      <td>6.967742</td>\n",
       "      <td>6.870968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>初中</th>\n",
       "      <td>23.416667</td>\n",
       "      <td>5.027778</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.833333</td>\n",
       "      <td>7.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大专或者本科</th>\n",
       "      <td>29.250000</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>9.187500</td>\n",
       "      <td>5.937500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小学</th>\n",
       "      <td>25.660714</td>\n",
       "      <td>5.375000</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>7.785714</td>\n",
       "      <td>6.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文盲</th>\n",
       "      <td>24.755102</td>\n",
       "      <td>5.306122</td>\n",
       "      <td>4.285714</td>\n",
       "      <td>5.959184</td>\n",
       "      <td>6.224490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硕士研究生及以上学历</th>\n",
       "      <td>23.814815</td>\n",
       "      <td>4.888889</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>6.962963</td>\n",
       "      <td>7.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高中</th>\n",
       "      <td>25.461538</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.461538</td>\n",
       "      <td>7.384615</td>\n",
       "      <td>6.461538</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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       "                   总分      PA总分      NA总分      PE总分      NE总分\n",
       "                   均值        均值        均值        均值        均值\n",
       "6、您的文化程度是：                                                   \n",
       "中专或技校       24.032258  5.096774  5.161290  6.967742  6.870968\n",
       "初中          23.416667  5.027778  5.000000  6.833333  7.444444\n",
       "大专或者本科      29.250000  6.250000  4.250000  9.187500  5.937500\n",
       "小学          25.660714  5.375000  4.750000  7.785714  6.750000\n",
       "文盲          24.755102  5.306122  4.285714  5.959184  6.224490\n",
       "硕士研究生及以上学历  23.814815  4.888889  4.666667  6.962963  7.370370\n",
       "高中          25.461538  5.000000  4.461538  7.384615  6.461538"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7、您现在的每个月收入（不包括子女给的）：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1000-2500元</th>\n",
       "      <td>26.457627</td>\n",
       "      <td>5.288136</td>\n",
       "      <td>3.932203</td>\n",
       "      <td>7.559322</td>\n",
       "      <td>6.457627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000元以上</th>\n",
       "      <td>26.090909</td>\n",
       "      <td>5.772727</td>\n",
       "      <td>3.954545</td>\n",
       "      <td>7.681818</td>\n",
       "      <td>7.409091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000元以下</th>\n",
       "      <td>24.853659</td>\n",
       "      <td>5.073171</td>\n",
       "      <td>4.585366</td>\n",
       "      <td>6.975610</td>\n",
       "      <td>6.609756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2500-4000元</th>\n",
       "      <td>23.264706</td>\n",
       "      <td>4.882353</td>\n",
       "      <td>5.323529</td>\n",
       "      <td>6.558824</td>\n",
       "      <td>6.852941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4000-5500元</th>\n",
       "      <td>24.333333</td>\n",
       "      <td>5.055556</td>\n",
       "      <td>4.944444</td>\n",
       "      <td>6.888889</td>\n",
       "      <td>6.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5500-7000元</th>\n",
       "      <td>25.045455</td>\n",
       "      <td>5.363636</td>\n",
       "      <td>4.681818</td>\n",
       "      <td>6.727273</td>\n",
       "      <td>6.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7000-8500元</th>\n",
       "      <td>23.423077</td>\n",
       "      <td>5.153846</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>7.153846</td>\n",
       "      <td>7.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8500-10000元</th>\n",
       "      <td>24.631579</td>\n",
       "      <td>5.578947</td>\n",
       "      <td>5.421053</td>\n",
       "      <td>7.105263</td>\n",
       "      <td>6.631579</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "                              总分      PA总分      NA总分      PE总分      NE总分\n",
       "                              均值        均值        均值        均值        均值\n",
       "7、您现在的每个月收入（不包括子女给的）：                                                   \n",
       "1000-2500元             26.457627  5.288136  3.932203  7.559322  6.457627\n",
       "10000元以上               26.090909  5.772727  3.954545  7.681818  7.409091\n",
       "1000元以下                24.853659  5.073171  4.585366  6.975610  6.609756\n",
       "2500-4000元             23.264706  4.882353  5.323529  6.558824  6.852941\n",
       "4000-5500元             24.333333  5.055556  4.944444  6.888889  6.666667\n",
       "5500-7000元             25.045455  5.363636  4.681818  6.727273  6.363636\n",
       "7000-8500元             23.423077  5.153846  5.500000  7.153846  7.384615\n",
       "8500-10000元            24.631579  5.578947  5.421053  7.105263  6.631579"
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>总分</th>\n",
       "      <th>PA总分</th>\n",
       "      <th>NA总分</th>\n",
       "      <th>PE总分</th>\n",
       "      <th>NE总分</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9、您和孩子之间的关系：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>关系一般</th>\n",
       "      <td>23.784615</td>\n",
       "      <td>4.953846</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.923077</td>\n",
       "      <td>7.092308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>关系较差</th>\n",
       "      <td>23.280000</td>\n",
       "      <td>4.840000</td>\n",
       "      <td>4.960000</td>\n",
       "      <td>7.240000</td>\n",
       "      <td>7.840000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>很亲近</th>\n",
       "      <td>27.049180</td>\n",
       "      <td>5.622951</td>\n",
       "      <td>4.114754</td>\n",
       "      <td>7.393443</td>\n",
       "      <td>5.852459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>比较亲近</th>\n",
       "      <td>24.811111</td>\n",
       "      <td>5.288889</td>\n",
       "      <td>4.733333</td>\n",
       "      <td>7.055556</td>\n",
       "      <td>6.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     总分      PA总分      NA总分      PE总分      NE总分\n",
       "                     均值        均值        均值        均值        均值\n",
       "9、您和孩子之间的关系：                                                   \n",
       "关系一般          23.784615  4.953846  5.000000  6.923077  7.092308\n",
       "关系较差          23.280000  4.840000  4.960000  7.240000  7.840000\n",
       "很亲近           27.049180  5.622951  4.114754  7.393443  5.852459\n",
       "比较亲近          24.811111  5.288889  4.733333  7.055556  6.800000"
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
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       "    <tr>\n",
       "      <th>10、您目前的身体状况：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>一般</th>\n",
       "      <td>24.588235</td>\n",
       "      <td>4.985294</td>\n",
       "      <td>4.852941</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.544118</td>\n",
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       "    <tr>\n",
       "      <th>比较健康</th>\n",
       "      <td>24.468354</td>\n",
       "      <td>5.189873</td>\n",
       "      <td>4.721519</td>\n",
       "      <td>7.012658</td>\n",
       "      <td>7.012658</td>\n",
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       "    <tr>\n",
       "      <th>身体不好</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.028571</td>\n",
       "      <td>5.085714</td>\n",
       "      <td>6.657143</td>\n",
       "      <td>7.600000</td>\n",
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       "    <tr>\n",
       "      <th>非常健康</th>\n",
       "      <td>27.135593</td>\n",
       "      <td>5.711864</td>\n",
       "      <td>4.152542</td>\n",
       "      <td>7.694915</td>\n",
       "      <td>6.118644</td>\n",
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       "                     总分      PA总分      NA总分      PE总分      NE总分\n",
       "                     均值        均值        均值        均值        均值\n",
       "10、您目前的身体状况：                                                   \n",
       "一般            24.588235  4.985294  4.852941  7.000000  6.544118\n",
       "比较健康          24.468354  5.189873  4.721519  7.012658  7.012658\n",
       "身体不好          23.000000  5.028571  5.085714  6.657143  7.600000\n",
       "非常健康          27.135593  5.711864  4.152542  7.694915  6.118644"
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       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11、您是否经常和朋友一起进行娱乐活动</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>从不</th>\n",
       "      <td>24.409091</td>\n",
       "      <td>5.060606</td>\n",
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       "      <td>7.121212</td>\n",
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       "      <td>25.093458</td>\n",
       "      <td>5.158879</td>\n",
       "      <td>4.495327</td>\n",
       "      <td>6.953271</td>\n",
       "      <td>6.523364</td>\n",
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       "    <tr>\n",
       "      <th>经常</th>\n",
       "      <td>25.220588</td>\n",
       "      <td>5.529412</td>\n",
       "      <td>4.764706</td>\n",
       "      <td>7.397059</td>\n",
       "      <td>6.941176</td>\n",
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       "                            总分      PA总分      NA总分      PE总分      NE总分\n",
       "                            均值        均值        均值        均值        均值\n",
       "11、您是否经常和朋友一起进行娱乐活动                                                   \n",
       "从不                   24.409091  5.060606  4.863636  7.121212  6.909091\n",
       "偶尔                   25.093458  5.158879  4.495327  6.953271  6.523364\n",
       "经常                   25.220588  5.529412  4.764706  7.397059  6.941176"
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       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "      <th>均值</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13、您是否有自己的兴趣爱好</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>否</th>\n",
       "      <td>23.477612</td>\n",
       "      <td>5.014925</td>\n",
       "      <td>5.022388</td>\n",
       "      <td>6.925373</td>\n",
       "      <td>7.440299</td>\n",
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       "      <th>是</th>\n",
       "      <td>26.775701</td>\n",
       "      <td>5.514019</td>\n",
       "      <td>4.233645</td>\n",
       "      <td>7.373832</td>\n",
       "      <td>5.878505</td>\n",
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      "text/plain": [
       "                       总分      PA总分      NA总分      PE总分      NE总分\n",
       "                       均值        均值        均值        均值        均值\n",
       "13、您是否有自己的兴趣爱好                                                   \n",
       "否               23.477612  5.014925  5.022388  6.925373  7.440299\n",
       "是               26.775701  5.514019  4.233645  7.373832  5.878505"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(性别)\n",
    "display(年龄)\n",
    "display(居住地)\n",
    "display(婚姻)\n",
    "display(居住方式)\n",
    "display(文化)\n",
    "display(收入)\n",
    "display(子女关系)\n",
    "display(身体)\n",
    "display(娱乐)\n",
    "display(兴趣)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 关联某些关键词查看影响程度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>均值</th>\n",
       "      <th>最大</th>\n",
       "      <th>最小</th>\n",
       "      <th>均值</th>\n",
       "      <th>最大</th>\n",
       "      <th>最小</th>\n",
       "      <th>均值</th>\n",
       "      <th>最大</th>\n",
       "      <th>最小</th>\n",
       "      <th>均值</th>\n",
       "      <th>最大</th>\n",
       "      <th>最小</th>\n",
       "      <th>均值</th>\n",
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       "      <th>最小</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1、您的性别：</th>\n",
       "      <th>2、您的年龄段：</th>\n",
       "      <th>6、您的文化程度是：</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">女</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">60-69岁</th>\n",
       "      <th>中专或技校</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>26</td>\n",
       "      <td>20</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>初中</th>\n",
       "      <td>21.750000</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大专或者本科</th>\n",
       "      <td>29.666667</td>\n",
       "      <td>42</td>\n",
       "      <td>23</td>\n",
       "      <td>9.333333</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>7</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小学</th>\n",
       "      <td>26.125000</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>7.875000</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文盲</th>\n",
       "      <td>22.600000</td>\n",
       "      <td>28</td>\n",
       "      <td>18</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硕士研究生及以上学历</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>29</td>\n",
       "      <td>19</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高中</th>\n",
       "      <td>28.333333</td>\n",
       "      <td>45</td>\n",
       "      <td>18</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">70-79岁</th>\n",
       "      <th>中专或技校</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "      <td>6.333333</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>5.833333</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>初中</th>\n",
       "      <td>22.666667</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333333</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大专或者本科</th>\n",
       "      <td>25.500000</td>\n",
       "      <td>28</td>\n",
       "      <td>23</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小学</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>8.400000</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文盲</th>\n",
       "      <td>28.571429</td>\n",
       "      <td>48</td>\n",
       "      <td>22</td>\n",
       "      <td>5.571429</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>5.857143</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>4.857143</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硕士研究生及以上学历</th>\n",
       "      <td>22.500000</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>4.250000</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>8.750000</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高中</th>\n",
       "      <td>25.125000</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>5.125000</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>4.875000</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>6.375000</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">80岁以上</th>\n",
       "      <th>中专或技校</th>\n",
       "      <td>22.571429</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>5.428571</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>5.857143</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>初中</th>\n",
       "      <td>25.533333</td>\n",
       "      <td>37</td>\n",
       "      <td>16</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>7.133333</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大专或者本科</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小学</th>\n",
       "      <td>25.250000</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>11</td>\n",
       "      <td>4</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文盲</th>\n",
       "      <td>26.500000</td>\n",
       "      <td>41</td>\n",
       "      <td>19</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硕士研究生及以上学历</th>\n",
       "      <td>22.666667</td>\n",
       "      <td>30</td>\n",
       "      <td>16</td>\n",
       "      <td>3.666667</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>8.166667</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高中</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>24</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>8.800000</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"21\" valign=\"top\">男</th>\n",
       "      <th rowspan=\"7\" valign=\"top\">60-69岁</th>\n",
       "      <th>中专或技校</th>\n",
       "      <td>21.000000</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>初中</th>\n",
       "      <td>20.714286</td>\n",
       "      <td>28</td>\n",
       "      <td>16</td>\n",
       "      <td>3.857143</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6.714286</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>7.857143</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大专或者本科</th>\n",
       "      <td>33.400000</td>\n",
       "      <td>48</td>\n",
       "      <td>24</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小学</th>\n",
       "      <td>22.416667</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>4.916667</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>6.583333</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>7.833333</td>\n",
       "      <td>11</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文盲</th>\n",
       "      <td>23.666667</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6.833333</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硕士研究生及以上学历</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>7.250000</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高中</th>\n",
       "      <td>25.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>19</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">70-79岁</th>\n",
       "      <th>中专或技校</th>\n",
       "      <td>23.375000</td>\n",
       "      <td>29</td>\n",
       "      <td>20</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>7.875000</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>7.375000</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>初中</th>\n",
       "      <td>20.666667</td>\n",
       "      <td>23</td>\n",
       "      <td>18</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6.666667</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8.666667</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大专或者本科</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小学</th>\n",
       "      <td>26.090909</td>\n",
       "      <td>39</td>\n",
       "      <td>19</td>\n",
       "      <td>5.090909</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>4.636364</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8.636364</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文盲</th>\n",
       "      <td>23.428571</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>4.428571</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>5.142857</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>5.285714</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硕士研究生及以上学历</th>\n",
       "      <td>26.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>21</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高中</th>\n",
       "      <td>19.500000</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>4</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">80岁以上</th>\n",
       "      <th>中专或技校</th>\n",
       "      <td>22.666667</td>\n",
       "      <td>30</td>\n",
       "      <td>18</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>5.666667</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>9</td>\n",
       "      <td>4</td>\n",
       "      <td>6.833333</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>初中</th>\n",
       "      <td>24.500000</td>\n",
       "      <td>31</td>\n",
       "      <td>20</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>大专或者本科</th>\n",
       "      <td>33.666667</td>\n",
       "      <td>44</td>\n",
       "      <td>20</td>\n",
       "      <td>7.333333</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>9.666667</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>小学</th>\n",
       "      <td>26.454545</td>\n",
       "      <td>46</td>\n",
       "      <td>20</td>\n",
       "      <td>6.727273</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>4.636364</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>7.545455</td>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>7.181818</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>文盲</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>6.111111</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>6.444444</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>8.222222</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>硕士研究生及以上学历</th>\n",
       "      <td>25.250000</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>高中</th>\n",
       "      <td>25.500000</td>\n",
       "      <td>31</td>\n",
       "      <td>22</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    总分              PA总分             NA总分      \\\n",
       "                                    均值  最大  最小        均值  最大 最小        均值  最大   \n",
       "1、您的性别： 2、您的年龄段： 6、您的文化程度是：                                                     \n",
       "女       60-69岁   中专或技校       23.000000  26  20  5.333333   6  5  5.333333   8   \n",
       "                 初中          21.750000  27  19  5.250000   8  2  6.250000   9   \n",
       "                 大专或者本科      29.666667  42  23  9.333333  10  8  6.000000  10   \n",
       "                 小学          26.125000  44  19  4.750000   9  2  4.250000   6   \n",
       "                 文盲          22.600000  28  18  5.200000   9  1  4.900000   8   \n",
       "                 硕士研究生及以上学历  24.000000  29  19  5.000000   6  4  5.000000   6   \n",
       "                 高中          28.333333  45  18  5.666667  10  3  4.000000   7   \n",
       "        70-79岁   中专或技校       29.000000  40  23  6.333333   9  3  3.500000   5   \n",
       "                 初中          22.666667  26  19  6.000000   8  4  5.333333   8   \n",
       "                 大专或者本科      25.500000  28  23  5.500000   7  4  4.000000   6   \n",
       "                 小学          28.000000  44  19  5.600000  10  2  4.400000   7   \n",
       "                 文盲          28.571429  48  22  5.571429  10  4  2.000000   6   \n",
       "                 硕士研究生及以上学历  22.500000  28  19  6.000000   9  4  4.250000   6   \n",
       "                 高中          25.125000  30  17  5.125000   9  2  4.875000   7   \n",
       "        80岁以上    中专或技校       22.571429  28  19  5.428571   8  4  5.857143   7   \n",
       "                 初中          25.533333  37  16  5.666667  10  3  4.200000   7   \n",
       "                 大专或者本科      21.000000  22  20  2.500000   4  1  6.500000   8   \n",
       "                 小学          25.250000  29  21  4.500000   6  4  5.750000   7   \n",
       "                 文盲          26.500000  41  19  5.800000  10  2  4.900000   8   \n",
       "                 硕士研究生及以上学历  22.666667  30  16  3.666667   6  1  4.333333   8   \n",
       "                 高中          27.000000  30  24  5.600000   7  4  4.800000   8   \n",
       "男       60-69岁   中专或技校       21.000000  21  21  3.000000   3  3  4.000000   4   \n",
       "                 初中          20.714286  28  16  3.857143   5  2  6.000000   8   \n",
       "                 大专或者本科      33.400000  48  24  6.200000  10  3  2.800000   7   \n",
       "                 小学          22.416667  27  18  4.916667   8  1  5.250000   8   \n",
       "                 文盲          23.666667  26  19  4.666667   7  2  4.666667   5   \n",
       "                 硕士研究生及以上学历  23.000000  27  18  4.750000   7  3  6.250000   8   \n",
       "                 高中          25.000000  30  19  4.500000   7  2  4.750000   6   \n",
       "        70-79岁   中专或技校       23.375000  29  20  4.375000   7  1  5.500000   8   \n",
       "                 初中          20.666667  23  18  3.000000   4  1  4.333333   5   \n",
       "                 大专或者本科      18.000000  18  18  3.000000   3  3  6.000000   6   \n",
       "                 小学          26.090909  39  19  5.090909  10  2  4.636364  10   \n",
       "                 文盲          23.428571  27  18  4.000000   8  0  4.428571   7   \n",
       "                 硕士研究生及以上学历  26.000000  30  21  4.500000   7  2  4.500000   7   \n",
       "                 高中          19.500000  23  16  3.500000   4  3  4.000000   5   \n",
       "        80岁以上    中专或技校       22.666667  30  18  4.666667   7  3  5.666667   7   \n",
       "                 初中          24.500000  31  20  5.250000   6  4  5.250000   8   \n",
       "                 大专或者本科      33.666667  44  20  7.333333   9  5  3.000000   5   \n",
       "                 小学          26.454545  46  20  6.727273  10  4  4.636364  10   \n",
       "                 文盲          24.000000  32  16  6.111111  10  3  4.333333  10   \n",
       "                 硕士研究生及以上学历  25.250000  32  19  6.000000   9  3  3.750000   5   \n",
       "                 高中          25.500000  31  22  4.750000   6  4  3.500000   6   \n",
       "\n",
       "                                     PE总分              NE总分         \n",
       "                            最小         均值  最大 最小         均值  最大 最小  \n",
       "1、您的性别： 2、您的年龄段： 6、您的文化程度是：                                         \n",
       "女       60-69岁   中专或技校       4   6.000000   8  3   7.000000   8  5  \n",
       "                 初中          4   6.000000   7  4   7.250000   8  5  \n",
       "                 大专或者本科      0  11.000000  14  7   8.666667  14  4  \n",
       "                 小学          0   7.875000  12  5   6.250000  11  1  \n",
       "                 文盲          2   4.600000   7  3   6.300000   9  3  \n",
       "                 硕士研究生及以上学历  4   7.400000  11  5   7.400000  11  6  \n",
       "                 高中          0   7.000000  11  5   4.333333   8  0  \n",
       "        70-79岁   中专或技校       1   8.000000   9  7   5.833333  10  1  \n",
       "                 初中          2   6.666667   8  5   8.666667  11  7  \n",
       "                 大专或者本科      2   9.500000  10  9   9.500000  11  8  \n",
       "                 小学          0   8.400000  12  5   5.600000  11  0  \n",
       "                 文盲          0   5.857143  14  2   4.857143   9  0  \n",
       "                 硕士研究生及以上学历  2   5.500000   7  3   8.750000  11  7  \n",
       "                 高中          1   7.250000  10  6   6.375000   9  4  \n",
       "        80岁以上    中专或技校       5   6.000000   9  3   7.000000   9  5  \n",
       "                 初中          0   7.200000  11  4   7.133333  12  2  \n",
       "                 大专或者本科      5   7.000000   7  7   6.000000   8  4  \n",
       "                 小学          4   8.000000  11  4   5.500000   7  4  \n",
       "                 文盲          3   7.200000  10  5   5.600000  11  0  \n",
       "                 硕士研究生及以上学历  3   7.500000  10  4   8.166667  12  4  \n",
       "                 高中          2   8.800000  10  7   6.600000  10  4  \n",
       "男       60-69岁   中专或技校       4   6.000000   6  6   8.000000   8  8  \n",
       "                 初中          3   6.714286  12  3   7.857143  12  4  \n",
       "                 大专或者本科      0   9.400000  14  5   3.400000   5  0  \n",
       "                 小学          2   6.583333   9  3   7.833333  11  6  \n",
       "                 文盲          4   6.500000   8  3   6.833333   9  4  \n",
       "                 硕士研究生及以上学历  4   7.750000  10  3   7.250000   9  5  \n",
       "                 高中          3   6.500000   9  5   5.250000   8  2  \n",
       "        70-79岁   中专或技校       3   7.875000  11  5   7.375000  10  5  \n",
       "                 初中          3   6.666667   8  5   8.666667  10  8  \n",
       "                 大专或者本科      6   5.000000   5  5   8.000000   8  8  \n",
       "                 小学          1   8.636364  14  5   7.000000  14  3  \n",
       "                 文盲          0   5.142857   9  0   5.285714   9  0  \n",
       "                 硕士研究生及以上学历  3   7.500000  10  5   5.500000   8  4  \n",
       "                 高中          3   6.000000   8  4  10.000000  12  8  \n",
       "        80岁以上    中专或技校       4   6.500000   9  4   6.833333  10  4  \n",
       "                 初中          3   6.750000  10  3   6.250000   9  3  \n",
       "                 大专或者本科      0   9.666667  14  3   4.333333   7  0  \n",
       "                 小学          0   7.545455  12  3   7.181818  11  0  \n",
       "                 文盲          0   6.444444  14  2   8.222222  14  2  \n",
       "                 硕士研究生及以上学历  1   5.750000   8  3   6.750000   9  5  \n",
       "                 高中          2   7.750000  10  5   7.500000  10  3  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "性别与文化 = df.groupby ( by = ['1、您的性别：','2、您的年龄段：','6、您的文化程度是：'] ) \\\n",
    "             .agg ({ \"总分\" : [\"mean\",\"max\",\"min\"], \\\n",
    "                     \"PA总分\":[\"mean\",\"max\",\"min\"], \\\n",
    "                     \"NA总分\":[\"mean\",\"max\",\"min\"], \\\n",
    "                     \"PE总分\":[\"mean\",\"max\",\"min\"], \\\n",
    "                     \"NE总分\":[\"mean\",\"max\",\"min\"],}) \\\n",
    "             .rename ( columns = {\"mean\":\"均值\",\"max\":\"最大\", \"min\":\"最小\"} )\n",
    "display(性别与文化)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 以相关表格建立条形统计图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1caed7b5220>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import rcParams\n",
    "rcParams['font.family'] = 'simhei'\n",
    "性别.reset_index().plot.bar(x = '1、您的性别：',y = \"总分\")\n",
    "年龄.reset_index().plot.bar(x = '2、您的年龄段：',y = \"总分\")\n",
    "居住地.reset_index().plot.bar(x = '3、您现在的居住地是：',y = \"总分\")\n",
    "婚姻.reset_index().plot.bar(x = '4、您的婚姻状况是：',y = \"总分\")\n",
    "居住方式.reset_index().plot.bar(x = '5、您的居住方式是：',y = \"总分\")\n",
    "文化.reset_index().plot.bar(x = '6、您的文化程度是：',y = \"总分\")\n",
    "收入.reset_index().plot.bar(x = '7、您现在的每个月收入（不包括子女给的）：',y = \"总分\")\n",
    "子女关系.reset_index().plot.bar(x = '9、您和孩子之间的关系：',y = \"总分\")\n",
    "身体.reset_index().plot.bar(x = '10、您目前的身体状况：',y = \"总分\")\n",
    "娱乐.reset_index().plot.bar(x = '11、您是否经常和朋友一起进行娱乐活动',y = \"总分\")\n",
    "兴趣.reset_index().plot.bar(x = '13、您是否有自己的兴趣爱好',y = \"总分\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
